Description: Objective: Effective strategies are needed to encourage smoking cessation for smokers without an intention to quit. We systematically reviewed the literature to investigate whether smoking reduction therapy can increase the long-term cessation rates of smokers without an intention to quit. Methods: PubMed, Embase, and CENTRAL (Cochrane Central Register of Controlled Trials) were searched for randomized controlled trials (RCTs) on the effect of smoking reduction therapy on long-term smoking cessation in smokers without an intention to quit. The primary outcome was the cessation rate at the longest follow-up period. A random effects model was used to calculate pooled relative risks (RRs) and 95% confidence intervals (CIs). Results: Fourteen trials with a total of 7981 smokers were included. The pooled analysis suggested that reduction support plus medication significantly increased the long-term cessation of smokers without an intention to quit compared to reduction support plus placebo (RR, 1.97; 95% CI, 1.44–2.7; I2, 52%) or no intervention (RR, 1.93; 95% CI, 1.41–2.64; I2, 46%). In a subgroup of smokers who received varenicline or nicotine replacement therapy (NRT), the differences were also statistically significant. This suggests the safety of using NRT. The percentage of smokers with serious adverse events who discontinued because of these events in the non-NRT group was slightly significantly different than in the control group. Insufficient evidence is available to test the efficacy of reduction behavioural support in promoting long-term cessation among this population. Conclusions: The present meta-analysis indicated the efficacy of NRT- and varenicline-assisted reduction to achieve complete cessation among smokers without an intention to quit. Further evidence is needed to assess the efficacy and safety of reduction behavioural support and bupropion.

Description: Planetary gearboxes exhibit complicated dynamic responses which are more difficult to detect in vibration signals than fixed-axis gear trains because of the special gear transmission structures. Diverse advanced methods have been developed for this challenging task to reduce or avoid unscheduled breakdown and catastrophic accidents. It is feasible to make fault features distinct by using multiwavelet denoising which depends on the feature separation and the threshold denoising. However, standard and fixed multiwavelets are not suitable for accurate fault feature detections because they are usually independent of the measured signals. To overcome this drawback, a method to construct customized multiwavelets based on the redundant symmetric lifting scheme is proposed in this paper. A novel indicator which combines kurtosis and entropy is applied to select the optimal multiwavelets, because kurtosis is sensitive to sharp impulses and entropy is effective for periodic impulses. The improved neighboring coefficients method is introduced into multiwavelet denoising. The vibration signals of a planetary gearbox from a satellite communication antenna on a measurement ship are captured under various motor speeds. The results show the proposed method could accurately detect the incipient pitting faults on two neighboring teeth in the planetary gearbox.

Description: The purpose of this study was to examine how different polarimetric parameters and an object-based approach influence the classification results of various land use/land cover types using fully polarimetric ALOS PALSAR data over coastal wetlands in Yancheng, China. To verify the efficiency of the proposed method, five other classifications (the Wishart supervised classification, the proposed method without polarimetric parameters, the proposed method without an object-based analysis, the proposed method without textural and geometric information and the proposed method using the nearest-neighbor classifier) were applied for comparison. The results indicated that some polarimetric parameters, such as Shannon entropy, Krogager_Kd, Alpha, HAAlpha_T11, VanZyl3_Vol, Derd, Barnes2_T33, polarization fraction, Barnes1_T33, Neuman_delta_mod and entropy, greatly improved the classification results. The shape index was a useful feature in distinguishing fish ponds and rivers. The distance to the sea can be regarded as an important factor in reducing the confusion between herbaceous wetland vegetation and grasslands. Furthermore, the decision tree algorithm increased the overall accuracy by 6.8% compared with the nearest neighbor classifier. This research demonstrated that different polarimetric parameters and the object-based approach significantly improved the performance of land cover classification in coastal wetlands using ALOS PALSAR data.